






"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE   =   {
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Tuple      =  """blip_text_model"""
					def __init__(							self							:	Optional[Any]     , UpperCAmelCase							:	Optional[Any]=3_0524     , UpperCAmelCase							:	Dict=768     , UpperCAmelCase							:	Any=768     , UpperCAmelCase							:	Dict=3072     , UpperCAmelCase							:	int=768     , UpperCAmelCase							:	Any=12     , UpperCAmelCase							:	Optional[int]=8     , UpperCAmelCase							:	Union[str, Any]=512     , UpperCAmelCase							:	int="gelu"     , UpperCAmelCase							:	List[Any]=1e-1_2     , UpperCAmelCase							:	Optional[int]=0.0     , UpperCAmelCase							:	List[Any]=0.0     , UpperCAmelCase							:	Dict=0.0_2     , UpperCAmelCase							:	List[str]=3_0522     , UpperCAmelCase							:	str=2     , UpperCAmelCase							:	List[str]=0     , UpperCAmelCase							:	Any=102     , UpperCAmelCase							:	Dict=True     , UpperCAmelCase							:	int=True     , **UpperCAmelCase							:	Tuple     , )	->       Optional[Any]:
											super().__init__(
											    pad_token_id=UpperCAmelCase     , bos_token_id=UpperCAmelCase     , eos_token_id=UpperCAmelCase     , sep_token_id=UpperCAmelCase     , **UpperCAmelCase     , )

											lowerCAmelCase   :Any						=			vocab_size
											lowerCAmelCase   :Union[str, Any]						=			hidden_size
											lowerCAmelCase   :Optional[Any]						=			encoder_hidden_size
											lowerCAmelCase   :List[str]						=			intermediate_size
											lowerCAmelCase   :List[Any]						=			projection_dim
											lowerCAmelCase   :List[Any]						=			hidden_dropout_prob
											lowerCAmelCase   :Optional[int]						=			num_hidden_layers
											lowerCAmelCase   :Dict						=			num_attention_heads
											lowerCAmelCase   :str						=			max_position_embeddings
											lowerCAmelCase   :List[str]						=			layer_norm_eps
											lowerCAmelCase   :Optional[Any]						=			hidden_act
											lowerCAmelCase   :Tuple						=			initializer_range
											lowerCAmelCase   :Optional[Any]						=			attention_probs_dropout_prob
											lowerCAmelCase   :Dict						=			is_decoder
											lowerCAmelCase   :Union[str, Any]						=			use_cache
					@classmethod
					def        UpperCAmelCase__ (							cls							:	List[Any]     , UpperCAmelCase							:	Union[str, os.PathLike]     , **UpperCAmelCase							:	Union[str, Any]   )	->       "PretrainedConfig":
											cls._set_token_in_kwargs(UpperCAmelCase   )

											lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			cls.get_config_dict(UpperCAmelCase     , **UpperCAmelCase   )

											# get the text config dict if we are loading from BlipConfig
											if config_dict.get('model_type'   ) == "blip":
																	lowerCAmelCase   :Optional[int]						=			config_dict['text_config']

											if "model_type" in config_dict and hasattr(cls     , 'model_type'   ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																	    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

											return cls.from_dict(UpperCAmelCase     , **UpperCAmelCase   )




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				int      =  """blip_vision_model"""
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Dict=768     , UpperCAmelCase							:	List[Any]=3072     , UpperCAmelCase							:	Any=512     , UpperCAmelCase							:	Any=12     , UpperCAmelCase							:	str=12     , UpperCAmelCase							:	str=384     , UpperCAmelCase							:	Dict=16     , UpperCAmelCase							:	str="gelu"     , UpperCAmelCase							:	int=1e-5     , UpperCAmelCase							:	int=0.0     , UpperCAmelCase							:	Tuple=1e-1_0     , **UpperCAmelCase							:	Tuple     , )	->       List[str]:
											super().__init__(**UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			hidden_size
											lowerCAmelCase   :Dict						=			intermediate_size
											lowerCAmelCase   :Optional[Any]						=			projection_dim
											lowerCAmelCase   :List[str]						=			num_hidden_layers
											lowerCAmelCase   :Optional[int]						=			num_attention_heads
											lowerCAmelCase   :Tuple						=			patch_size
											lowerCAmelCase   :Optional[int]						=			image_size
											lowerCAmelCase   :List[Any]						=			initializer_range
											lowerCAmelCase   :int						=			attention_dropout
											lowerCAmelCase   :Tuple						=			layer_norm_eps
											lowerCAmelCase   :Union[str, Any]						=			hidden_act
					@classmethod
					def        UpperCAmelCase__ (							cls							:	Optional[Any]     , UpperCAmelCase							:	Union[str, os.PathLike]     , **UpperCAmelCase							:	List[Any]   )	->       "PretrainedConfig":
											cls._set_token_in_kwargs(UpperCAmelCase   )

											lowerCAmelCase	, lowerCAmelCase   :List[str]						=			cls.get_config_dict(UpperCAmelCase     , **UpperCAmelCase   )

											# get the vision config dict if we are loading from BlipConfig
											if config_dict.get('model_type'   ) == "blip":
																	lowerCAmelCase   :List[str]						=			config_dict['vision_config']

											if "model_type" in config_dict and hasattr(cls     , 'model_type'   ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																	    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

											return cls.from_dict(UpperCAmelCase     , **UpperCAmelCase   )




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				List[str]      =  """blip"""
					lowercase_    :				Optional[int]      =  True
					def __init__(							self							:	Dict     , UpperCAmelCase							:	Dict=None     , UpperCAmelCase							:	Optional[int]=None     , UpperCAmelCase							:	Tuple=512     , UpperCAmelCase							:	List[str]=2.6_5_9_2     , UpperCAmelCase							:	Tuple=256     , **UpperCAmelCase							:	Tuple     , )	->       Union[str, Any]:
											super().__init__(**UpperCAmelCase   )

											if text_config is None:
																	lowerCAmelCase   :Tuple						=			{}
																	logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'   )

											if vision_config is None:
																	lowerCAmelCase   :Any						=			{}
																	logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'   )

											lowerCAmelCase   :List[Any]						=			BlipTextConfig(**UpperCAmelCase   )
											lowerCAmelCase   :Any						=			BlipVisionConfig(**UpperCAmelCase   )

											lowerCAmelCase   :Union[str, Any]						=			self.vision_config.hidden_size

											lowerCAmelCase   :Any						=			projection_dim
											lowerCAmelCase   :int						=			logit_scale_init_value
											lowerCAmelCase   :List[str]						=			1.0
											lowerCAmelCase   :int						=			0.0_2
											lowerCAmelCase   :Optional[Any]						=			image_text_hidden_size
					@classmethod
					def        UpperCAmelCase__ (							cls							:	Optional[Any]     , UpperCAmelCase							:	BlipTextConfig     , UpperCAmelCase							:	BlipVisionConfig     , **UpperCAmelCase							:	int   )	->       Tuple:
											return cls(text_config=text_config.to_dict()     , vision_config=vision_config.to_dict()     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       List[str]:
											lowerCAmelCase   :Any						=			copy.deepcopy(self.__dict__   )
											lowerCAmelCase   :Union[str, Any]						=			self.text_config.to_dict()
											lowerCAmelCase   :Dict						=			self.vision_config.to_dict()
											lowerCAmelCase   :Dict						=			self.__class__.model_type
											return output







"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			len(a__ )
						print('The following activities are selected:' )

						# The first activity is always selected
						lowerCAmelCase   :Dict						=			0
						print(a__     ,      end=',' )

						# Consider rest of the activities
						for j in range(a__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(a__     ,      end=',' )
																		lowerCAmelCase   :List[str]						=			j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   [1, 3, 0, 5, 8, 5]
		__SCREAMING_SNAKE_CASE   =   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)